Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
Personalization in advanced driver assistance systems and autonomous vehicles: A review
M Hasenjäger, H Wersing - 2017 ieee 20th international …, 2017 - ieeexplore.ieee.org
The field of advanced driver assistance systems (ADAS) has matured towards more and
more complex assistance functions, applied with wider scope and a strongly increasing …
more complex assistance functions, applied with wider scope and a strongly increasing …
Few-shot preference learning for human-in-the-loop rl
DJ Hejna III, D Sadigh - Conference on Robot Learning, 2023 - proceedings.mlr.press
While reinforcement learning (RL) has become a more popular approach for robotics,
designing sufficiently informative reward functions for complex tasks has proven to be …
designing sufficiently informative reward functions for complex tasks has proven to be …
From 'automation'to 'autonomy': the importance of trust repair in human–machine interaction
Modern interactions with technology are increasingly moving away from simple human use
of computers as tools to the establishment of human relationships with autonomous entities …
of computers as tools to the establishment of human relationships with autonomous entities …
Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving
A model used for velocity control during car following is proposed based on reinforcement
learning (RL). To optimize driving performance, a reward function is developed by …
learning (RL). To optimize driving performance, a reward function is developed by …
[图书][B] Active preference-based learning of reward functions
Our goal is to efficiently learn reward functions encoding a human's preferences for how a
dynamical system should act. There are two challenges with this. First, in many problems it is …
dynamical system should act. There are two challenges with this. First, in many problems it is …
Inverse preference learning: Preference-based rl without a reward function
Reward functions are difficult to design and often hard to align with human intent. Preference-
based Reinforcement Learning (RL) algorithms address these problems by learning reward …
based Reinforcement Learning (RL) algorithms address these problems by learning reward …
[HTML][HTML] Human-like driving behaviour emerges from a risk-based driver model
Current driving behaviour models are designed for specific scenarios, such as curve driving,
obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse …
obstacle avoidance, car-following, or overtaking. However, humans can drive in diverse …
Learning reward functions by integrating human demonstrations and preferences
Our goal is to accurately and efficiently learn reward functions for autonomous robots.
Current approaches to this problem include inverse reinforcement learning (IRL), which …
Current approaches to this problem include inverse reinforcement learning (IRL), which …
Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: Effects of automated driving styles and driver's driving styles
Automated Vehicle (AV) technology has the potential to significantly improve driver safety.
Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and …
Unfortunately, drivers could be reluctant to ride with AVs due to their lack of trust and …